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Record W7066252068

Good to Great How : Marine Harvest Can Innovate to Grow, and Grow to Innovate Amid Imperfect Industry Structure and Regulation

2018· dissertation· en· W7066252068 on OpenAlexaboutno aff

Bibliographic record

VenueDuo Research Archive (University of Oslo) · 2018
Typedissertation
Languageen
FieldArts and Humanities
TopicMedieval Philosophy and Theology
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)SustainabilityImperfectExternalityInvestment (military)ShareholderAgricultureProduction (economics)
DOInot available

Abstract

fetched live from OpenAlex

Spatial conflicts exist between firms and farms in the Norwegian Salmon Farming industry which perpetuates sea lice and disease issues and prevents the industry from growing. Lack of growth in Norway in the face of increasing demand, increases prices and reveals national market power. As salmon are treated for sea lice more often, the fish grow less, and die more frequently thus exacerbating this effect. As a result, consumer surplus goes down and dead loss goes up as the industry becomes less efficient. Gaps in the underlying regulatory framework and lack of coordination among firms allows this to continue and thus jeopardizes sustainability of the industry. As a result, Marine Harvest Norway Farming (MHNF), being 20 – 25% of the industry volume, is disproportionately affected. This may put the Marine Harvest Group (MHG) strategy in jeopardy, as without growth, it becomes increasingly difficult for MHG to build customer and shareholder value. In this thesis I identify and analyse the spatial externalities, identify the regulatory gaps, and make suggestions for improvements. Further I analyse the effect on MHNF and how it jeopardizes the MHG strategy. A strategic analysis of MHNF reveals that they have the resources and existing strategy to deal with some of the gaps and externalities but not all, and only if they execute well and on time. Further I suggest how the strategy can be improved by focusing on a few key points including: increasing their government lobby for tougher lice rules and more coordinated site fallows, improving their RAS technology by bringing it in-house through purchase of a key supplier, focusing on wrasse culture, and by closing all waiting cages and open well boat transport. Further, I suggest forming joint ventures with smaller farmers to coordinate operations, developing the use of in-sea post smolt systems by bringing it in-house through purchase of a key supplier, and using more robust nets and sterile salmon. Next, I analyse the opportunity to expand to Newfoundland (NL), and determine that NL has the desired institutional surroundings for expansion, and that MHG has the correct resources and management to conduct the expansion, but that it is not without risks. Lastly, I conduct an innovation creation exercise, using a global cross-functional design team from MHG, to develop a hard to copy and profitable innovation for the new business unit, Marine Harvest Atlantic Canada. The product is made from pre-rigor fillet, is antibiotic free, is ASC certified, and from salmon operations monitored by the RSPCA for fish welfare. Finally, I determine that given MHG’s resources and global knowledge, they do not have to choose between growth in Norway or abroad but can do both. In Norway they can take specific actions to separate their fish in space and time from other farms, to avoid spatial conflict and therefore facilitate growth. In addition, MHG can employ cross-functional design teams in all BU’s to create buy-in across functional areas, to help develop successful hard-to-copy innovations and brands, thereby potentially moving the company from a good salmon farming company to a great seafood company, and thus building both customer and shareholder value.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.006
Scholarly communication0.0090.006
Open science0.0010.005
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0140.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.258
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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